In this work,
we build on the insight that although objects in images may not be naturally textureless,
their motions typically are.
Hence, we develop the self-supervised object segmentation model by exploiting their optical flows,
where the nuance in visual appearance is discarded,
thus not restricted to simple synthetic cases.
As an initial trial, we experimented with the same setting as [1]},
where query vectors are sampled from a Gaussian distribution;
however, we were unable to train it.
Instead, we use a learnable embedding here,
which we highlight as one of the architectural changes
critical to our model's success.
Other critical changes include instance normalization and temporal consistency, which we demonstrate in ablations in Section REF .
